Face recognition is one of the most difficult and challenging tasks in computer vision, partly because of large variations in human faces. Difficulty and challenge is even higher for face age-estimation. Researchers have been developing technologies for face age-estimation due to the demands of many real-world operating scenarios that require accurate, efficient, uncooperative, and cost-effective solutions, such as automated control and surveillance systems. Accurate age-estimation may be of great benefit to businesses, such as convenience stores, restaurants, and others, who are required to forbid underage access to, for example, alcohol or tobacco. Age-estimation systems can also be applicable in homeland security technologies, criminal identification, management of e-documents and electronic customer relationships, all without requiring imposing password prompts, password change reminders, etc. In restaurants and other businesses, age-recognition systems may be used help to identify trends in business relative to the ages of customers. Additionally, these systems can help to prevent children from viewing or otherwise consuming unacceptable media or programming and can even be used to thwart underage people from driving cars before they reach a legal driving age.
Aging of human faces is a complicated process influenced by many factors such as gender, ethnicity, heredity factors and environmental factors, including cosmetic interventions, societal pressure, relative sun exposure, and drug or alcohol consumption. In this process, there are some controllable factors (i.e., gender, ethnicity, heredity, etc.) that can be exploited in order to recognize trends in the aging of human faces. However, other uncontrollable factors, such as environment, living styles, and sun exposure (photoaging), can prove quite challenging to deal with. Therefore, correctly estimating an age from a face is a huge challenge even for humans, let alone for computing devices.
The effects of age on the human face has been studied in numerous research fields, including orthodontics, anthropology, anatomy, forensic art, and cognitive psychology. However, compared to these aging-related fields, computer science approaches for aging problems are relatively new. From the viewpoint of computer science, face aging technologies generally address two areas: face age-estimation and face age-progression. The face age-estimation problem can be addressed with computer software that has the ability to recognize the ages of individuals in a given photo. Meanwhile, the face age-progression problem has the ability to predict the future faces of an individual in a given photo.
To achieve an accurate, efficient, uncooperative, and cost-effective solution to the problem of face age-estimation, it becomes necessary to extract as much unique information as possible from each image in question and to use such information in an exhaustive comparison. However, these methods are known to be computationally expensive and may require special tweaking in order to generate meaningful results. More accurate and efficient face recognition methods are desired in numerous applications, including those discussed above, which demand near real-time computation and do not require user cooperation.